In recent years, natural language processing (NLP) has got great development with deep
learning techniques. In the sub-field of machine translation, a new approach named Neural …

Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that
parameters are endowed with distributions which may themselves introduce new …

Language understanding research is held back by a failure to relate language to the
physical world it describes and to the social interactions it facilitates. Despite the incredible …

We propose a new benchmark corpus to be used for measuring progress in statistical
language modeling. With almost one billion words of training data, we hope this benchmark …

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian
framework for modeling axial data (ie, observations are axes of direction) that can be …

What are the units of text that we want to model? From bytes to multi-word expressions, text
can be analyzed and generated at many granularities. Until recently, most natural language …

Is there a principled way to learn a probabilistic discriminative classifier from an unlabeled
data set? We present a framework that simultaneously clusters the data and trains a …

The Dirichlet process is a stochastic proces used in Bayesian nonparametric models of data,
particularly in Dirichlet process mixture models (also known as infinite mixture models). It is …

Ensembles of randomized decision trees, usually referred to as random forests, are widely
used for classification and regression tasks in machine learning and statistics. Random …

We develop the distance dependent Chinese restaurant process, a flexible class of
distributions over partitions that allows for dependencies between the elements. This class …